Three-dimensional Neural Net for Learning Visuomotor-Coordination of a Robot Arm

Martinetz T, Ritter H, Schulten K (1990)
IEEE-Transactions on Neural Networks 1(1): 131-136.

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Abstract
An extension of T. Kohonen's (1982) self-organizing mapping algorithm together with an error-correction scheme based on the Widrow-Hoff learning rule is applied to develop a learning algorithm for the visuomotor coordination of a simulated robot arm. Learning occurs by a sequence of trial movements without the need for an external teacher. Using input signals from a pair of cameras, the closed robot arm system is able to reduce its positioning error to about 0.3% of the linear dimensions of its work space. This is achieved by choosing the connectivity of a three-dimensional lattice consisting of the units of the neural net.
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Martinetz T, Ritter H, Schulten K. Three-dimensional Neural Net for Learning Visuomotor-Coordination of a Robot Arm. IEEE-Transactions on Neural Networks. 1990;1(1):131-136.
Martinetz, T., Ritter, H., & Schulten, K. (1990). Three-dimensional Neural Net for Learning Visuomotor-Coordination of a Robot Arm. IEEE-Transactions on Neural Networks, 1(1), 131-136.
Martinetz, T., Ritter, H., and Schulten, K. (1990). Three-dimensional Neural Net for Learning Visuomotor-Coordination of a Robot Arm. IEEE-Transactions on Neural Networks 1, 131-136.
Martinetz, T., Ritter, H., & Schulten, K., 1990. Three-dimensional Neural Net for Learning Visuomotor-Coordination of a Robot Arm. IEEE-Transactions on Neural Networks, 1(1), p 131-136.
T. Martinetz, H. Ritter, and K. Schulten, “Three-dimensional Neural Net for Learning Visuomotor-Coordination of a Robot Arm”, IEEE-Transactions on Neural Networks, vol. 1, 1990, pp. 131-136.
Martinetz, T., Ritter, H., Schulten, K.: Three-dimensional Neural Net for Learning Visuomotor-Coordination of a Robot Arm. IEEE-Transactions on Neural Networks. 1, 131-136 (1990).
Martinetz, Thomas, Ritter, Helge, and Schulten, Klaus. “Three-dimensional Neural Net for Learning Visuomotor-Coordination of a Robot Arm”. IEEE-Transactions on Neural Networks 1.1 (1990): 131-136.
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